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Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Mutual information model selection algorithm for time series.

Elif Akça1, Ceylan Yozgatlıgil2

  • 1Research Centre for Operations Research and Business Statistics, KU Leuven, Leuven, Belgium.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for selecting Box-Jenkins time series models. It uses penalized mutual information to improve model accuracy and forecasting performance.

Keywords:
Box–Jenkins modelsmutual informationorder selection

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Area of Science:

  • Statistics
  • Time Series Analysis
  • Econometrics

Background:

  • Accurate time series forecasting is crucial for decision-making.
  • Selecting the optimal model is essential for reliable predictions.
  • Existing methods for Box-Jenkins model order selection have limitations.

Purpose of the Study:

  • To develop a novel algorithm for Box-Jenkins model order selection.
  • To enhance the accuracy of time series forecasting.
  • To provide a more robust alternative to current model selection techniques.

Main Methods:

  • The proposed algorithm combines the penalized natural logarithm of mutual information.
  • Mutual information is calculated between the original series and predictions from candidate models.
  • Penalization is achieved by subtracting the number of parameters and empirical information.

Main Results:

  • Simulation studies demonstrated the algorithm's effectiveness across various scenarios.
  • Application to real datasets confirmed its satisfactory performance.
  • The algorithm showed promising results compared to existing methods.

Conclusions:

  • The developed algorithm offers a valuable new tool for Box-Jenkins time series model selection.
  • It provides a promising and satisfactory alternative for improving forecast accuracy.
  • Further research can explore its application in more complex time series models.